An active learning framework for efficient condition severity classification

Nir Nissim, Mary Regina Bol, , Robert Moskovitch, Nicholas P Tatonetti, Yuval Elovici, Yuval Shahar, George Hripcsak

Artificial Intelligence in Medicine: 15th Conference on Artificial …, 2015

Understanding condition severity, as extracted from Electronic Health Records (EHRs), is important for many public health purposes. Methods requiring physicians to annotate condition severity are time-consuming and costly. Previously, a passive learning algorithm called CAESAR was developed to capture severity in EHRs. This approach required physicians to label conditions manually, an exhaustive process. We developed a framework that uses two Active Learning (AL) methods (Exploitation and Combination_XA) to decrease manual labeling efforts by selecting only the most informative conditions for training. We call our approach CAESAR-Active Learning Enhancement (CAESAR-ALE). As compared to passive methods,CAESAR-ALE’s first AL method, Exploitation, reduced labeling efforts by 64% and achieved an equivalent true positive rate, while CAESAR-ALE’s second AL method …